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What you'd own:
- The viewer: Cornerstone3D, VTK.js, volume rendering, reformats, progressive loading, radiological correctness.
- The annotation system: 3D mask storage, AI-assisted segmentation, evaluation pipeline, agreement scorecards.
- Annotation schemas: per-lesion-category schemas with gating, conditionally required fields, clinical decisions with radiologists.
Who we're looking for:
- Someone who has built software used for hours a day and improved it by watching usage.
- Comfortable with coordinate systems, DICOM geometry, and failing safely with patient data.
- Can disagree with a radiologist about software but defer on medicine.
Stack:
- Frontend: React, TypeScript, Vite, Cornerstone3D, VTK.js, Jotai, TanStack Query, Tailwind, Playwright.
- Backend: Python, FastAPI, pytest, AWS (DynamoDB, S3, Cognito, SQS, ECS, Lambda), Terraform.
- Data: DICOM, DICOMweb, HTJ2K, NIfTI, RLE.
a2z Radiology AI
a2z Radiology AI builds clinical AI that reads alongside radiologists, with an FDA-cleared abdomen-pelvis CT triage device. The team includes engineers from MIT and Stanford and fellowship-trained radiologists, backed by Khosla Ventures.